The use of nasogastric (NG) tubes in acute, critical, and long-term care may lead to mechanical, infectious, and metabolic complications. NG intubation is a risk factor for aspiration and complications of organ injury. Mechanical complications include deliberate self-extubation and accidental extubation, both of which comprise unplanned extubation and occur in >35% of cases in rehabilitation rooms. Therefore, we intend to propose a digital warning tool to detect NG tube dislodgment over several days or weeks for a continuous insertion of the NG tube. On the basis of fog computing, integrating dexter-to-sinister light-controlled sensors and fuzzy Petri net (FPN) was performed to achieve the proposed assistant tool. The proposed intelligent algorithm can also be easily implemented using a high-level programming language (Language C/C++) in an embedded system. The experimental results demonstrated the feasibility of the algorithm under normal conditions and partial and NG two-tube dislodgments.